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test_classification_classimblearn.py
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import os
import pytest
H5PY_NOT_AVAIL = False
try:
import h5py
except ImportError:
H5PY_NOT_AVAIL = True
IMBLEARN_NOT_AVAIL = False
try:
import imblearn
except ImportError:
IMBLEARN_NOT_AVAIL = True
DATA_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data")
CLASSIFICATION_DATA_DIR = os.path.join(DATA_DIR, "classification")
@pytest.mark.skipif(
(H5PY_NOT_AVAIL or IMBLEARN_NOT_AVAIL),
reason="h5py or imblearn dependencies not available",
)
def test_rand_oversample_smpls(tmp_path):
import rsgislib.classification
import rsgislib.classification.classimblearn
cls_in_info = dict()
cls_in_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_forest_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_grass_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_urban_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_water_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info = dict()
cls_out_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(tmp_path, "cls_forest_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(tmp_path, "cls_grass_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(tmp_path, "cls_urban_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(tmp_path, "cls_water_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
rsgislib.classification.classimblearn.rand_oversample_smpls(
cls_in_info, cls_out_info
)
assert (
os.path.exists(os.path.join(tmp_path, "cls_forest_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_grass_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_urban_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_water_out_smpls.h5"))
)
@pytest.mark.skipif(
(H5PY_NOT_AVAIL or IMBLEARN_NOT_AVAIL),
reason="h5py or imblearn dependencies not available",
)
def test_smote_oversample_smpls(tmp_path):
import rsgislib.classification
import rsgislib.classification.classimblearn
cls_in_info = dict()
cls_in_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_forest_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_grass_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_urban_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_water_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info = dict()
cls_out_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(tmp_path, "cls_forest_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(tmp_path, "cls_grass_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(tmp_path, "cls_urban_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(tmp_path, "cls_water_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
rsgislib.classification.classimblearn.smote_oversample_smpls(
cls_in_info, cls_out_info
)
assert (
os.path.exists(os.path.join(tmp_path, "cls_forest_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_grass_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_urban_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_water_out_smpls.h5"))
)
@pytest.mark.skipif(
(H5PY_NOT_AVAIL or IMBLEARN_NOT_AVAIL),
reason="h5py or imblearn dependencies not available",
)
def test_adasyn_oversample_smpls(tmp_path):
import rsgislib.classification
import rsgislib.classification.classimblearn
cls_in_info = dict()
cls_in_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_forest_smpls.h5"),
red=120,
green=120,
blue=120,
)
# Use same training input file so there must be 'neighbours'.
cls_in_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_forest_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info = dict()
cls_out_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(tmp_path, "cls_forest_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(tmp_path, "cls_grass_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
rsgislib.classification.classimblearn.adasyn_oversample_smpls(
cls_in_info, cls_out_info
)
assert os.path.exists(
os.path.join(tmp_path, "cls_forest_out_smpls.h5")
) and os.path.exists(os.path.join(tmp_path, "cls_grass_out_smpls.h5"))
@pytest.mark.skipif(
(H5PY_NOT_AVAIL or IMBLEARN_NOT_AVAIL),
reason="h5py or imblearn dependencies not available",
)
def test_borderline_smote_oversample_smpls(tmp_path):
import rsgislib.classification
import rsgislib.classification.classimblearn
cls_in_info = dict()
cls_in_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_forest_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_grass_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_urban_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_water_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info = dict()
cls_out_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(tmp_path, "cls_forest_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(tmp_path, "cls_grass_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(tmp_path, "cls_urban_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(tmp_path, "cls_water_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
rsgislib.classification.classimblearn.borderline_smote_oversample_smpls(
cls_in_info, cls_out_info
)
assert (
os.path.exists(os.path.join(tmp_path, "cls_forest_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_grass_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_urban_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_water_out_smpls.h5"))
)
@pytest.mark.skipif(
(H5PY_NOT_AVAIL or IMBLEARN_NOT_AVAIL),
reason="h5py or imblearn dependencies not available",
)
def test_cluster_centroid_undersample_smpls(tmp_path):
import rsgislib.classification
import rsgislib.classification.classimblearn
cls_in_info = dict()
cls_in_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_forest_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_grass_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_urban_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_water_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info = dict()
cls_out_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(tmp_path, "cls_forest_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(tmp_path, "cls_grass_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(tmp_path, "cls_urban_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(tmp_path, "cls_water_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
rsgislib.classification.classimblearn.cluster_centroid_undersample_smpls(
cls_in_info, cls_out_info
)
assert (
os.path.exists(os.path.join(tmp_path, "cls_forest_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_grass_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_urban_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_water_out_smpls.h5"))
)
@pytest.mark.skipif(
(H5PY_NOT_AVAIL or IMBLEARN_NOT_AVAIL),
reason="h5py or imblearn dependencies not available",
)
def test_random_undersample_smpls(tmp_path):
import rsgislib.classification
import rsgislib.classification.classimblearn
cls_in_info = dict()
cls_in_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_forest_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_grass_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_urban_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_water_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info = dict()
cls_out_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(tmp_path, "cls_forest_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(tmp_path, "cls_grass_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(tmp_path, "cls_urban_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(tmp_path, "cls_water_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
rsgislib.classification.classimblearn.random_undersample_smpls(
cls_in_info, cls_out_info
)
assert (
os.path.exists(os.path.join(tmp_path, "cls_forest_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_grass_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_urban_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_water_out_smpls.h5"))
)
@pytest.mark.skipif(
(H5PY_NOT_AVAIL or IMBLEARN_NOT_AVAIL),
reason="h5py or imblearn dependencies not available",
)
def test_near_miss_undersample_smpls(tmp_path):
import rsgislib.classification
import rsgislib.classification.classimblearn
cls_in_info = dict()
cls_in_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_forest_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_grass_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_urban_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_water_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info = dict()
cls_out_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(tmp_path, "cls_forest_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(tmp_path, "cls_grass_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(tmp_path, "cls_urban_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(tmp_path, "cls_water_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
rsgislib.classification.classimblearn.near_miss_undersample_smpls(
cls_in_info, cls_out_info
)
assert (
os.path.exists(os.path.join(tmp_path, "cls_forest_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_grass_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_urban_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_water_out_smpls.h5"))
)
@pytest.mark.skipif(
(H5PY_NOT_AVAIL or IMBLEARN_NOT_AVAIL),
reason="h5py or imblearn dependencies not available",
)
def test_edited_near_neigh_undersample_smpls(tmp_path):
import rsgislib.classification
import rsgislib.classification.classimblearn
cls_in_info = dict()
cls_in_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_forest_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_grass_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_urban_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_water_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info = dict()
cls_out_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(tmp_path, "cls_forest_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(tmp_path, "cls_grass_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(tmp_path, "cls_urban_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(tmp_path, "cls_water_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
rsgislib.classification.classimblearn.edited_near_neigh_undersample_smpls(
cls_in_info, cls_out_info
)
assert (
os.path.exists(os.path.join(tmp_path, "cls_forest_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_grass_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_urban_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_water_out_smpls.h5"))
)
@pytest.mark.skipif(
(H5PY_NOT_AVAIL or IMBLEARN_NOT_AVAIL),
reason="h5py or imblearn dependencies not available",
)
def test_repeat_edited_near_neigh_undersample_smpls(tmp_path):
import rsgislib.classification
import rsgislib.classification.classimblearn
cls_in_info = dict()
cls_in_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_forest_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_grass_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_urban_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_water_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info = dict()
cls_out_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(tmp_path, "cls_forest_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(tmp_path, "cls_grass_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(tmp_path, "cls_urban_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(tmp_path, "cls_water_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
rsgislib.classification.classimblearn.borderline_smote_oversample_smpls(
cls_in_info, cls_out_info
)
assert (
os.path.exists(os.path.join(tmp_path, "cls_forest_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_grass_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_urban_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_water_out_smpls.h5"))
)
@pytest.mark.skipif(
(H5PY_NOT_AVAIL or IMBLEARN_NOT_AVAIL),
reason="h5py or imblearn dependencies not available",
)
def test_all_knn_undersample_smpls(tmp_path):
import rsgislib.classification
import rsgislib.classification.classimblearn
cls_in_info = dict()
cls_in_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_forest_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_grass_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_urban_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_water_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info = dict()
cls_out_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(tmp_path, "cls_forest_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(tmp_path, "cls_grass_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(tmp_path, "cls_urban_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(tmp_path, "cls_water_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
rsgislib.classification.classimblearn.borderline_smote_oversample_smpls(
cls_in_info, cls_out_info
)
assert (
os.path.exists(os.path.join(tmp_path, "cls_forest_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_grass_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_urban_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_water_out_smpls.h5"))
)
@pytest.mark.skipif(
(H5PY_NOT_AVAIL or IMBLEARN_NOT_AVAIL),
reason="h5py or imblearn dependencies not available",
)
def test_condensed_near_neigh_undersample_smpls(tmp_path):
import rsgislib.classification
import rsgislib.classification.classimblearn
cls_in_info = dict()
cls_in_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_forest_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_grass_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_urban_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_water_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info = dict()
cls_out_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(tmp_path, "cls_forest_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(tmp_path, "cls_grass_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(tmp_path, "cls_urban_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(tmp_path, "cls_water_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
rsgislib.classification.classimblearn.condensed_near_neigh_undersample_smpls(
cls_in_info, cls_out_info
)
assert (
os.path.exists(os.path.join(tmp_path, "cls_forest_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_grass_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_urban_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_water_out_smpls.h5"))
)
@pytest.mark.skipif(
(H5PY_NOT_AVAIL or IMBLEARN_NOT_AVAIL),
reason="h5py or imblearn dependencies not available",
)
def test_one_sided_sel_undersample_smpls(tmp_path):
import rsgislib.classification
import rsgislib.classification.classimblearn
cls_in_info = dict()
cls_in_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_forest_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_grass_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_urban_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_water_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info = dict()
cls_out_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(tmp_path, "cls_forest_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(tmp_path, "cls_grass_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(tmp_path, "cls_urban_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(tmp_path, "cls_water_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
rsgislib.classification.classimblearn.one_sided_sel_undersample_smpls(
cls_in_info, cls_out_info
)
assert (
os.path.exists(os.path.join(tmp_path, "cls_forest_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_grass_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_urban_out_smpls.h5"))
and os.path.exists(os.path.join(tmp_path, "cls_water_out_smpls.h5"))
)
@pytest.mark.skipif(
(H5PY_NOT_AVAIL or IMBLEARN_NOT_AVAIL),
reason="h5py or imblearn dependencies not available",
)
def test_neighbourhood_clean_undersample_smpls(tmp_path):
import rsgislib.classification
import rsgislib.classification.classimblearn
cls_in_info = dict()
cls_in_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_forest_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_grass_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Urban"] = rsgislib.classification.ClassSimpleInfoObj(
id=3,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_urban_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_in_info["Water"] = rsgislib.classification.ClassSimpleInfoObj(
id=4,
file_h5=os.path.join(CLASSIFICATION_DATA_DIR, "cls_water_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info = dict()
cls_out_info["Forest"] = rsgislib.classification.ClassSimpleInfoObj(
id=1,
file_h5=os.path.join(tmp_path, "cls_forest_out_smpls.h5"),
red=120,
green=120,
blue=120,
)
cls_out_info["Grass"] = rsgislib.classification.ClassSimpleInfoObj(
id=2,
file_h5=os.path.join(tmp_path, "cls_grass_out_smpls.h5"),
red=120,
green=120,